Human-quality text summarization systems are difficult to design, and
even more difficult to evaluate, in part because documents can differ
along several dimensions, such as length, writing style and lexical
usage. Nevertheless, certain cues can often help suggest the selection
of sentences for inclusion in a summary. This paper presents our
analysis of news-article summaries generated by sentence selection.
Sentences are ranked for potential inclusion in the summary using a
weighted combination of statistical and linguistic features. This paper
analyzes some of the potential linguistic features -- derived from an
analysis of news-wire summaries -- for relative effectiveness. To
evaluate these features we use a modified version of precision-recall
curves, with a baseline derived from a theoretical analysis of text-span
overlap based on random selection. We illustrate our discussions with
empirical results showing the importance of discussing evaluation
results in the context of both corpus characteristics and compression
ratios.

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